Failure-Resistant Path Selection Considering Netizens’ Sentiment Orientation Under Typhoon Disasters

Zhenning Zhou, Jiaqi Yu, Gao Gao, Zhengfeng Huang (), Jintao Han and Pengjun Zheng
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Zhenning Zhou: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
Jiaqi Yu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
Gao Gao: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
Zhengfeng Huang: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
Jintao Han: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
Pengjun Zheng: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China

Sustainability, 2024, vol. 16, issue 23, 1-20

Abstract: After a typhoon disaster, selecting effective paths is crucial for ensuring the efficiency of emergency rescue operations and the distribution of essential supplies, which are vital for sustainable disaster response and community resilience. Current research into disaster scenarios is less aligned with actual scenarios as road conditions are hard to predict. This paper, set against the backdrop of typhoon disasters, employs netizens’ sentiment data to indirectly assess post-disaster road conditions and refine the calculation formula for road failure probabilities. This approach aims to identify failure-resistant paths to guide disaster relief decisions, thereby supporting sustainable disaster relief operations and minimizing resource expenditure. First, an expression form for road segment failure probability is established, considering factors such as tree falls, landslides, and waterlogging. Second, negative sentiment coefficients, derived from social media data analysis, are used to adjust road failure probabilities, reflecting the sentiments of affected communities. Then, a failure-resistant path selection model based on these adjusted road failure probabilities is proposed to enhance the resilience and sustainability of emergency transport paths. Finally, the model’s effectiveness is validated using Typhoon “In-Fa” in Ningbo as a case study.

Keywords: failure-resistant path selection model; sentiment analysis; emergency path planning; road failure probability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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